1. Field of the Invention
The present invention is related to allocating shared resources and more particularly to selecting an optimal set of templates for satisfying resource requests with minimal over and under provisioning.
2. Background Description
Acquiring and managing Information Technology (IT) is a major budgetary concern for any modern organization. Moreover, local IT hardware is seldom used at full capacity. So to reduce IT infrastructure costs and waste, instead of acquiring physical hardware, organizations are increasingly consolidating workload on virtual machines (VMs) hosted on fewer servers. A remote server computer provides each VM as a virtual server with virtual resources, e.g., processing power, memory and disk space. Typically, each VM configuration is selected from a number of virtual resource templates (VRTs or templates). Each VRT defines predetermined virtual resource capabilities, assignable to define a VM. So, the server computer(s) allocates capacity (e.g. disk space, processing resources and memory) to each VM by assigning a VRT that is most closely configured (software stack and licenses) for the VM's intended purpose and expected needs.
In managing these VMs it has been difficult to determine their optimal capacity and an optimal configuration, i.e., selecting the optimal VRT. Typically, a service provider selects a VRT and allocates corresponding physical resources for each VM, primarily, based on provider system optimization, workload predictions and resource usage history collected from continuously monitoring VM resource usage. Even good prediction results, however, can impair user experience due to over or under allocation. Over-allocation wastes energy and resources, capacity that would otherwise be available to other users or for supporting additional VMs. Because under-allocation allocates inadequate resources to one or more VMs, it impacts Quality-of-Service (QoS) on those VMs, e.g., halting video or garbled audio.
User requirements are highly variable which may force providers to vary the definition and number of templates they offer. While resource providers can increase the number of offered VRTs to meet all requests, increasing the number can cause resource overprovisioning and template sprawling. Resource overprovisioning, like over-allocation, consumes more resources and energy than is necessary for the provided capacity; that excess resource and energy could otherwise be made available to other users or for additional VMs. Template sprawling, also known as image sprawl, occurs when one template or image that fits one user's needs is tweaked to suit another, adding another template to the offered VRTs. Eventually, the number of templates expands to an unmanageable number.
Several approaches to matching application server requests to available resources have been tried. The typical cloud computing approach has been to select a VM template that most closely matches the requirements of the target user application ignoring the cost of the selection. Some other prior approaches specify and store VM templates and network templates, primarily, to accelerate installing VMs, associated networks and required software. Still other approaches have created profiles of running VMs and selecting target migration servers. Alternately, rather than focus on selecting virtual resource templates, other techniques focus on scaling applications to the templates, i.e., fitting the application to the VM rather than vice versa. Finally, a state of the art approach matches user provided Open Virtualization Format (OVF) instances to appropriate cloud offerings.
Thus, there is a need for allocating adequate IT resources for a minimum cost and without wasting resources, while also maintaining server QoS, and more particularly, there is a need for selecting a set of VM templates for provisioning VMs in cloud infrastructure, templates that efficiently satisfy the majority of user requirements for minimal cost.